The effectiveness of federated learning in high-performance information systems and informatics-based solutions for addressing current information support requirements is demonstrated in this book. To address heterogeneity challenges in Internet of Things (IoT) contexts, Federated Learning for Smart Communication using IoT Application analyses the development of personalized federated learning algorithms capable of mitigating the detrimental consequences of heterogeneity in several dimensions. It includes case studies of IoT-based human activity recognition to show the efficacy of personalized federated learning for intelligent IoT applications.

Features:

Demonstrates how federated learning offers a novel approach to building personalized models from data without invading users' privacy Describes how federated learning may assist in understanding and learning from user behavior in IoT applications while safeguarding user privacy Presents a detailed analysis of current research on federated learning, providing the reader with a broad understanding of the area Analyses the need for a personalized federated learning framework in cloud-edge and wireless-edge architecture for intelligent IoT applications Comprises real-life case illustrations and examples to help consolidate understanding of topics presented in each chapter

This book is recommended for anyone interested in federated learning-based intelligent algorithms for smart communications.